Huang, Junhui

Kaewunruen, Sakdirat

English

This poster presents the research wok into advanced machine learning using train-track dynamic condition and data to identify railway track quality, safety and reliability

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  University of BirminghamRailway track quality, safety and reliability throughtrain-track dynamics and advanced machinelearningHuang, Junhui; Kaewunruen, SakdiratLicense:Creative Commons: Attribution (CC BY)Citation for published version (Harvard):Huang, J & Kaewunruen, S 2023, 'Railway track quality, safety and reliability through train-track dynamics andadvanced machine learning', Clarke Lecture 2023, Birmingham, United Kingdom, 22/06/23 - 22/06/23.Link to publication on Research at Birmingham portalGeneral rightsUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or thecopyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposespermitted by law.•Users may freely distribute the URL that is used to identify this publication.•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of privatestudy or non-commercial research.•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)•Users may not further distribute the material nor use it for the purposes of commercial gain.Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version.Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has beenuploaded in error or has been deemed to be commercially or otherwise sensitive.If you believe that this is the case for this document, please contact UBIRA@lists.bham.ac.uk providing details and we will remove access tothe work immediately and investigate.Download date: 03. Aug. 2023www.bham.ac.uk s.kaewunruen@bham.ac.ukResearch QuestionsMethods1. How do characteristics, quality and reliability  of railway tracks impact system performance?2. What are the key factors influencing the tracks in dynamic operational conditions?3. What benefits of leveraging AI in enhancing the performance of track?4. How can AI be applied to determine real-time track conditions?Contextual BackgroundLimitations of traditional methodsRailway tracks are subjected to dynamic forces and environmental factors. AI can be used to monitor train-trackResults1. Both the performance of the standard CNN and dilated CNN are above 0.90.2. Considered factors: two different model types, three sample size, irregularities included.3. Dilated CNN produces the best R2 of 0.97 on the longest sample size.AI model develop3Model evaluation4Data pre-process2Data collection1• Synthesized dataset • Field data collection• Remove noise• Data bias management• Fill null• Standard CNN• Dilated CNN• Technical robustness• K-fold• R2• RMSE• MAE321, 250(161, 500)(81, 1000)6.6s1.6s32.5s16.9s1.5s1.1sRaw data(Samples, size)R2: 0.94MAE: 49.4R2: 0.93MAE: 54.8R2: 0.94MAE: 56.3R2: 0.95MAE: 47.8R2: 0.97MAE: 32.0R2: 0.97MAE: 38.9Junhui Huang, Dr Sakdirat Kaewunruen, School of EngineeringRailway track quality, safety & reliability through train-track dynamics and advanced machine intelligenceWhat have been achieved?Conclusions1. Huang J, Yin X, Kaewunruen S. Quantification of Dynamic Track Stiffness Using Machine Learning[J]. IEEEAccess, 2022, 10: 78747-78753.2. Huang J. and Kaewunruen, S. Evaluation of Railway Passenger Comfort With Machine Learning, in IEEE Access, vol. 10, pp. 2372-2381, 2022, doi: 10.1109/ACCESS.2021.3139465.3. Huang J and Kaewunruen S. Train-ride quality evaluation of the Elizabeth Line using machinelearning. Front. Built Environ. 8:1034433. doi: 10.3389/fbuil.2022.10344331. We have proposed a dilated CNN using the sensory dataacquired from axle box which is immune to speed up to 120km/h and bad weather conditions as there are sophisticatedaccelerometers designed to tackle the extreme weatherconditions.2. We have confirmed that the performance of the proposed modelis highly satisfied, the dilated technique contributes to savingcomputational cost, and the consideration of irregularitiesmakes no significant difference to the model's performance.3. An implication of this study is the possibility that wirelessaccelerometers can be mounted onboard service trains.4. New smartphone App has been developed to assess real-timeride comfort using machine intelligence.Key PublicationsThe Findings in ContextNew mobile-phone App for Real-time Ride Comfort AssessmentAI models provide satisfied performanceFast calculation with full technical robustnessEasy to implement and to connect real-time via 5G Reducing cost, and Empowering passengersNew methodEasy to useAccurateRobust

Railway track quality, safety and reliability through train-track dynamics and advanced machine learning

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Railway track quality, safety and reliability through train-track dynamics and advanced machine learning

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